Locally optimum Bayes detection in nonadditive non-Gaussian noise

The locally optimum Bayes theory of signal detection in additive non-Gaussian noise/interference is extended to independent observations of the received data without the additive noise restriction. The methodology employed parallels very closely the original development of threshold detection theory and utilizes the mathematical machinery of asymptotic decision theory, especially, the concepts of contiguity and locally asymptotically normal (LAN) log-likelihood ratio, which are needed in the determination of the detector structure in both coherent and incoherent modes and its statistics under both hypotheses. Under the present framework, the canonical (in signal waveform and noise statistics) optimum detection algorithms retain their asymptotically optimum character. An example is provided in order to demonstrate the applicability of the theory to a specific noise environment, where explicit forms of the non-linearities involved and numerical values of the new noise indices are obtained. Moreover, a significant improvement in performance (0 (24-27) dB) over that of optimum detectors in independent, additive Gaussian and non-Gaussian noise is noted.<>